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- Language: English
- Creators: Ira A. Fulton Schools of Engineering
Description
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Introduction/Purpose: the purpose of this study was to explore the perception of care after stillbirth and the use of physical activity and/or mindful approaches (e.g., yoga) to cope with grief in women of racial/ethnic minority who have experienced stillbirth.
Methods: This was an exploratory qualitative research study. Participants were African American, Hispanic, Asian, and American Indian women, between the ages of 26 and 38, who have experienced stillbirth within the past 3 years. Participants completed a 20-30 minute phone interview.
Results: Fourteen women participated in the study (M age = 31.02 ± 5.97 years; M time since stillbirth = 1.47 ± 0.94 years). Women’s perceptions about physical activity and mindfulness to cope with grief were coded into the following major themes: perception of health care after stillbirth (satisfaction with the level of care provided), recommendations about inter-conception health care from physician (relating to mental, emotional, and physical health), grief (comfort with communicating with the physician), coping mechanisms, perception of the relationship between physical activity and mood, barriers to participating in physical activity (social and behavioral), pre-pregnancy physical activity, and perception of mindful approach (e.g., yoga) as a coping mechanism.
Conclusion: This was the first study to explore perceptions of health care and the use of physical activity and/or mindful approaches (e.g., yoga) to cope with grief after stillbirth in women of racial/ethnic minority. Findings from this study may help inform health care professionals alter their care practices and introduce physical activity and mindfulness based approaches as coping mechanisms to mothers of stillborn babies.
Methods: This was an exploratory qualitative research study. Participants were African American, Hispanic, Asian, and American Indian women, between the ages of 26 and 38, who have experienced stillbirth within the past 3 years. Participants completed a 20-30 minute phone interview.
Results: Fourteen women participated in the study (M age = 31.02 ± 5.97 years; M time since stillbirth = 1.47 ± 0.94 years). Women’s perceptions about physical activity and mindfulness to cope with grief were coded into the following major themes: perception of health care after stillbirth (satisfaction with the level of care provided), recommendations about inter-conception health care from physician (relating to mental, emotional, and physical health), grief (comfort with communicating with the physician), coping mechanisms, perception of the relationship between physical activity and mood, barriers to participating in physical activity (social and behavioral), pre-pregnancy physical activity, and perception of mindful approach (e.g., yoga) as a coping mechanism.
Conclusion: This was the first study to explore perceptions of health care and the use of physical activity and/or mindful approaches (e.g., yoga) to cope with grief after stillbirth in women of racial/ethnic minority. Findings from this study may help inform health care professionals alter their care practices and introduce physical activity and mindfulness based approaches as coping mechanisms to mothers of stillborn babies.
ContributorsArvayo, Jordan Michelle (Author) / Huberty, Jennifer (Thesis director) / Hoffner, Kristin (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2015-05
Description
This study examined the effect of an 8-week exercise intervention on functional exercise capacity in adolescents with Down syndrome (DS). Forty participants were randomly assigned to one of three groups: assisted cycling (ACT) (n = 17) where participants experienced at least a 35% increase in their voluntary cycling speed through the use of a motor, voluntary cycling (VC) (n = 15) where participants cycled at a self-selected cadence, and no cycling (NC) (n = 8) where participants did not participate in any cycling intervention. In each cycling intervention, each participant completed three, 30 minute cycling sessions per week for a total of eight weeks. The Six-Minute Walk Test (6MWT) was administered prior to and after the 8-week intervention in pre-test and post-test assessment sessions, respectively. Our hypothesis was somewhat supported in that functional exercise capacity improved after ACT as measured by an increase in total number of laps walked, total distance walked, and average walking speed during the 6MWT, when compared to VC or NC.
ContributorsCook, Megan Rey (Author) / Ringenbach, Shannon (Thesis director) / Huberty, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2015-05
Description
Objective: Fewer than 50% of female college freshmen meet physical activity (PA) guidelines. Innovative approaches that help college women increase their PA are warranted. The study purpose was to pilot test a magazine-based discussion group for improving PA, self-worth, and nutrition behaviors in freshmen college females. Method: Thirty-seven women (18-20 years) were randomized to intervention (n=17) and control (n=20) groups. The intervention group participated in an 8-week magazine-based discussion group adapted from a previously tested social cognitive theory based intervention, Fit Minded. Excerpts from a popular women's health magazine were discussed during weekly meetings incorporating PA, self-worth and nutrition education. The control group did not attend meetings, but received the magazines. Outcomes and feasibility measures included: self-reported PA, general self-worth, knowledge self-worth, self-efficacy, social support, and daily fruits, vegetables, junk food, sugar-sweetened beverage consumption. Results: Twelve participants from the intervention group attended more than 75% of meetings. A time effect was observed for PA (p=0.001) and family social support (p=0.002). Time x group effects were observed for PA (p=0.001), general self-worth (p=0.04), knowledge self-worth (p=0.03), and daily sugar-sweetened beverage consumption (p=0.03), with the intervention group reporting greater increases in PA, general self-worth and knowledge self-worth and greater decreases in daily sugar-sweetened beverage consumption. Although not significant, the intervention group demonstrated positive trends in self-efficacy, friend social support and fruit and veggie consumption as compared to the control group. Conclusion: A magazine-based discussion group may provide a promising platform to improve PA, self-worth and nutrition behaviors in female college freshmen.
ContributorsPellitteri, Katelyn (Author) / Huberty, Jennifer (Thesis director) / Bruening, Meg (Committee member) / Barrett, The Honors College (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / School of Social Transformation (Contributor) / Sandra Day O'Connor College of Law (Contributor)
Created2014-05
Description
Despite the fact that seizures are commonly associated with autism spectrum disorder (ASD), the effectiveness of treatments for seizures has not been well studied in individuals with ASD. This manuscript reviews both traditional and novel treatments for seizures associated with ASD. Studies were selected by systematically searching major electronic databases and by a panel of experts that treat ASD individuals. Only a few anti-epileptic drugs (AEDs) have undergone carefully controlled trials in ASD, but these trials examined outcomes other than seizures. Several lines of evidence point to valproate, lamotrigine, and levetiracetam as the most effective and tolerable AEDs for individuals with ASD. Limited evidence supports the use of traditional non-AED treatments, such as the ketogenic and modified Atkins diet, multiple subpial transections, immunomodulation, and neurofeedback treatments. Although specific treatments may be more appropriate for specific genetic and metabolic syndromes associated with ASD and seizures, there are few studies which have documented the effectiveness of treatments for seizures for specific syndromes. Limited evidence supports l-carnitine, multivitamins, and N-acetyl-l-cysteine in mitochondrial disease and dysfunction, folinic acid in cerebral folate abnormalities and early treatment with vigabatrin in tuberous sclerosis complex. Finally, there is limited evidence for a number of novel treatments, particularly magnesium with pyridoxine, omega-3 fatty acids, the gluten-free casein-free diet, and low-frequency repetitive transcranial magnetic simulation. Zinc and l-carnosine are potential novel treatments supported by basic research but not clinical studies. This review demonstrates the wide variety of treatments used to treat seizures in individuals with ASD as well as the striking lack of clinical trials performed to support the use of these treatments. Additional studies concerning these treatments for controlling seizures in individuals with ASD are warranted.
ContributorsFrye, Richard E. (Author) / Rossignol, Daniel (Author) / Casanova, Manuel F. (Author) / Brown, Gregory L. (Author) / Martin, Victoria (Author) / Edelson, Stephen (Author) / Coben, Robert (Author) / Lewine, Jeffrey (Author) / Slattery, John C. (Author) / Lau, Chrystal (Author) / Hardy, Paul (Author) / Fatemi, S. Hossein (Author) / Folsom, Timothy D. (Author) / MacFabe, Derrick (Author) / Adams, James (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-09-13